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rtemis (version 0.79)

s.RFSRC: Random Forest for Classification, Regression, and Survival [C, R, S]

Description

Train a Random Forest for Regression, Classification, or Survival Regression using randomForestSRC

Usage

s.RFSRC(x, y = NULL, x.test = NULL, y.test = NULL, x.name = NULL,
  y.name = NULL, n.trees = 1000, weights = NULL, ipw = TRUE,
  ipw.type = 2, upsample = FALSE, upsample.seed = NULL,
  bootstrap = "by.root", mtry = NULL, importance = TRUE,
  proximity = TRUE, nodesize = if (!is.null(y) && !is.factor(y)) 5 else
  1, nodedepth = NULL, na.action = "na.impute", trace = FALSE,
  print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  rtclass = NULL, verbose = TRUE, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)

Arguments

x

Numeric vector or matrix of features, i.e. independent variables

y

Numeric vector of outcome, i.e. dependent variable

x.test

(Optional) Numeric vector or matrix of validation set features must have set of columns as x

y.test

(Optional) Numeric vector of validation set outcomes

n.trees

Integer: Number of trees to grow. The more the merrier.

bootstrap

String:

mtry

Integer: Number of features sampled randomly at each split

outdir

Optional. Path to directory to save output

...

Additional arguments to be passed to randomForestSRC::rfsrc

Value

Object of class rtMod

Details

For Survival Regression, y must be an object of type Surv, created using survival::Surv(time, status) mtry is the only tunable parameter, but it usually only makes a small difference and is often not tuned.

See Also

elevate for external cross-validation

Other Supervised Learning: s.ADABOOST, s.ADDTREE, s.BART, s.BAYESGLM, s.BRUTO, s.C50, s.CART, s.CTREE, s.DA, s.ET, s.EVTREE, s.GAM.default, s.GAM.formula, s.GAMSEL, s.GAM, s.GBM3, s.GBM, s.GLMNET, s.GLM, s.GLS, s.H2ODL, s.H2OGBM, s.H2ORF, s.IRF, s.KNN, s.LDA, s.LM, s.MARS, s.MLRF, s.MXN, s.NBAYES, s.NLA, s.NLS, s.NW, s.POLYMARS, s.PPR, s.PPTREE, s.QDA, s.QRNN, s.RANGER, s.RF, s.SGD, s.SPLS, s.SVM, s.TFN, s.XGBLIN, s.XGB

Other Tree-based methods: s.ADABOOST, s.ADDTREE, s.BART, s.C50, s.CART, s.CTREE, s.ET, s.EVTREE, s.GBM3, s.GBM, s.H2OGBM, s.H2ORF, s.IRF, s.MLRF, s.PPTREE, s.RANGER, s.RF, s.XGB